PPT

Theory of Computing
Lecture 6
MAS 714
Hartmut Klauck
Traversing Graphs
• We are given a graph G=(V,E)
• Starting vertex s
• The goal is to traverse the graph, i.e., to visit each
vertex at least once
– For example to find a marked vertex t or decide if t is
reachable from s
• Two variants:
– Breadth First Search (BFS)
– Depth First Search (DFS)
Traversing Graphs
• Common to both procedures:
– Use a datastructure with the following operations:
• Insert a vertex
• Remove a vertex
– Maintain an active vertex (start with s)
– Maintain an array of vertices already visited
– Then:
• Insert all (unvisited) neighbors of the active vertex,
mark it as visited
• Remove a vertex v and make it active
The Datastructure
• We distinguish by the rule that determines the
next active vertex
• Alternative 1: queue
– FIFO (first in first out)
• Alternative 2: stack
– LIFO (last in first out)
Result
• Alternative 1: FIFO
– Breadth First Search
– Neighbors of s will be visited before their
neighbors etc.
• Alternative 2: LIFO
– Depth First Search
– Insert neighbors, last neighbor becomes active,
then insert his neighbors, last neighbor becomes
active etc.
Traversing Graphs
• With both methods eventually all reachable
vertices are visited
• Different applications:
– BFS can be used to find shorted paths in
unweighted graphs
– DFS can be used to topologically sort a directed
acyclic graph
Depth First Search
• If we use a stack as datastructure we get
Depth First Search (DFS)
• Typically, DFS will maintain some extra
information:
– Time when v is put on the stack
– Time, when all neighbors of v have been examined
• This information is useful for applications
Datastructure: Stack
• A stack is a linked list together with two
operations
– push(x,S): Insert element x at the front of the list S
– pop(S): Remove the front element of the list S
• Implementation:
– Need to maintain only the pointer to the front of
the stack
– Useful to also have
• peek(S): Find the front element but don’t remove
Digression: Recursion and Stacks
• Our model of Random Access Machines does
not directly allow recursion
– Neither does any real hardware
• Compilers will “roll out” recursive calls
– Put all local variables of the calling procedure in a
safe place
– Execute the call
– Return the result and restore the local variables
Recursion
• The best datastructure for this is a stack
– Push all local variables to the stack
– LIFO functionality is exactly the right thing
• Example: Recursion tree of Quicksort
DFS
• Procedure:
1. For all v:
• ¼(v)=NIL, d(v)=0, f(v)=0
2. Enter s into the stack S, set TIME=1, d(s)=TIME
3. While S is not empty
a) v=peek(S)
b) Find the first neighbor w of v with d(w)=0:
–
push(w,S) , ¼(w)=v, TIME=TIME+1, d(w)=TIME
c) If there is no such w: pop(S), TIME=TIME+1, f(v)=TIME
DFS
• The array d(v) holds the time we first visit a
vertex.
• The array f(v) holds the time when all
neighbors of v have been processed
• “discovery” and “finish”
• In particular, when d(v)=0 then v has not been
found yet
Simple Observations
• Vertices are given d(v) numbers between
1 and 2n
• Each vertex is put on the stack once, and
receives the f(v) number once all neighbors
are visited
• Running time is O(n+m)
A recursive DFS
• Stacks are there to roll out recursion
• Consider the procedure in a recursive way!
• Furthermore we can start DFS from all unvisited
vertices to traverse the whole graph, not just the
vertices reachable from s
• We also want to label edges
– The edges in (¼(v),v) form trees: tree edges
– We can label all other edges as
• back edges
• cross edges
• forward edges
Edge classification
• Lemma: the edges (¼(v),v) form a tree
• Definition:
– Edges going down along a path in a tree (but not tree
edge) are forward edges
– Edges going up along a path in a tree are
back edges
– Edges across paths/tree are
cross edges
• A vertex v is a descendant of u if there is a path of tree
edges from u to v
• Observation: descendants are discovered after their
“ancestors” but finish before them
Example: edge labeling
• Tree edges, Back edges, Forward edges,
Cross edges
Recursive DFS
• DFS(G):
1. TIME=1 (global variable)
2. For all v: ¼(v)=NIL, d(v)=0, f(v)=0
3. For all v: if d(v)=0 then DFS(G,v)
• DFS(G,v)
1. TIME=TIME+1, d(v)=TIME
2. For all neighbors w of v:
1.
2.
3.
If d(w)=0 then (v,w) is tree edge, DFS(G,w)
If d(w)0 and f(w)0 then cross edge or forward edge
If d(w)0 and f(w)=0 then back edge
3. TIME=TIME+1, f(v)=TIME
Recursive DFS
• How to decide if forward or cross edge?
– Assume, (v,w) is an edge and f(w) is not 0
– If d(w)>d(v) then forward edge
– If d(v)>d(w) then cross edge
Application 1: Topological Sorting
• A DAG is a directed acyclic graph
– A partial order on vertices
• A topological sorting of a DAG is a numbering
of vertices s.t. all edges go from smaller to
larger vertices
– A total order that is consistent with the partial
order
DAGs
• Lemma: G is a DAG iff there are no back edges
– Proof:
•
•
•
•
•
•
•
If there is a back edge, then there is a cycle
The other direction: suppose there is a cycle c
Let u be the first vertex discovered on c
v is u’s predecessor on c
Then v is a descendant of u, i.e., d(v)>d(u) and f(v)<f(u)
When edge (v,u) is processed: f(u)=0, d(v)>d(u)
Thus (v,u) is a back edge
Topological Sorting
• Algorithm:
– Output the vertices in the reverse order of the f(v)
as the topological sorting of G
• I.e., put the v into a list when they finish in
DFS, so that the last finished vertex is first in
list
Topological Sorting
• We need to prove correctness
–
–
–
–
–
Certainly we provide a total ordering of vertices
Now assume vertex i is smaller than j in the ordering
I.e., i finished after j
Need to show: there is no path from j to i
Proof:
• j finished means all descendants of j are finished
• Hence i is not a descendant of j (otherwise i finishes first)
• If j is a descendant of i then a path from j to i must contain a
back edge (but those do not exist in a DAG)
• If j is not a descendant of i then a path from j to i contains a
cross edge, but then f(i)< f(j)
Topological Sorting
• Hence we can compute a topological sorting
in linear time
Application 2: Strongly connected
components
• Definition:
– A strongly connected component of a graph G is a
maximal set of vertices V’ such that for each pair
of vertices v,w in V’ there is a path from v to w
• Note: in undirected graphs this corresponds to
connected components, but here we have
one-way roads
• Strongly connected components (viewed as
vertices) form a DAG inside a graph G
Strongly connected components
• Algorithm
– Use DFS(G) to compute finish times f(v) for all v
– Compute GT [Transposed graph: edges (u,v)
replaced by (v,u)]
– Run DFS(GT), but in the DFS procedure go over
vertices in order of decreasing f(v) from the first
DFS
– Vertices in a tree generated by DFS(GT) form a
strongly connected component
Strongly connected components
• Time: O(n+m)
• We skip the correctness proof
• Note the usefulness of the f(v) numbers
computed in the first DFS